We present a novel method for monocular hand shape and pose estimation at
unprecedented runtime performance of 100fps and at state-of-the-art accuracy.
This is enabled by a new learning based architecture designed such that it can
make use of all the sources of available hand training data: image data with
either 2D or 3D annotations, as well as stand-alone 3D animations without
corresponding image data. It features a 3D hand joint detection module and an
inverse kinematics module which regresses not only 3D joint positions but also
maps them to joint rotations in a single feed-forward pass. This output makes
the method more directly usable for applications in computer vision and
graphics compared to only regressing 3D joint positions. We demonstrate that
our architectural design leads to a significant quantitative and qualitative
improvement over the state of the art on several challenging benchmarks. Our
model is publicly available for future research.

本研究提出了一种能够在 100fps 下具有最先进精度的单目手部形状和姿态估计新方法，它采用的是一种新的基于学习的架构设计，使其能够利用三维或二维标注图像数据以及独立的三维动画等所有可用的手部训练数据。该方法的输出使其更适用于计算机视觉和图形学领域中的应用，并且在数个具有挑战性的基准测试中取得了显著的定量和定性改善。